speech-emotion-recognition-system

做语音识别时用到DTW 算法。便于初学者的学习。(DTW algorithm is used for speech recognition. Easy for beginners to learn.)

LD3320 是一颗基于非特定人语音识别（SI-ASR：Speaker-Independent Automatic Speech Recognition）技术的语音识别/声控芯片。提供了真正的单 芯片语音识别解决方案。 LD3320 芯片上集成了高精度的A/D 和D/A 接口，不再需要外接辅助的 Flash 和RAM，即可以实现语音识别/声控/人机对话功能。并且，识别的关键词 语列表是可以动态编辑的。(Voice Recognition ；Speech Recognition ；Speech recognition technology )

EVC 基于CE的TTS源码，需安装语音库(TTS resource code based on wince)

关于如何使用fpga实现语音识别 最新版本(err)

基于最小均方误差的助听器语音去噪算法, 用于以最大程度消除助听器接受的语音杂音.(Speech denoising algorithm based on the minimum mean square error of a hearing aid for the hearing aid acceptable voice murmur eliminated to the greatest extent.)

最经典的基于ICA实现的语音信号的采集、随机混合，再通过盲分离将混合后的语音信号分离(The most classical ICA-based speech signal acquisition, random mixing, and then the mixed speech signal is separated by blind separation.)

基于DTW算法实现孤立数字及连续数字语句的识别，识别率还可以（内含语音数据集）(Based on DTW algorithm, isolated number and continuous number statements can be recognized, and the recognition rate can also be (including voice data set))

通常语音信号在增强时会出现混响现象，演讲者为了消除背景混响，不得不频繁地偏转头部的方向，这样会造成脉冲响应的不断改变。我们结合盲解卷法和频谱消去法来提高逆滤波器的滤波效果。我们利用输入语音信号间的相关系数矩阵计算出稳定、精确的室内脉冲响应的逆滤波器，而这些输入信号无需测量室内的脉冲响应就能被观测到。逆滤波能够消除早期的反射，这些反射包含混响中的绝大部分能量。之后，用频谱消去法来抑制逆滤波后的信号的尾部混响。本方法在实际适应性方面的表现通过具体的实验进行了验证，结果表明盲解卷法和频谱消去法的结合相较于单独使用一种方法，能够提供一个更优越的演说环境。(Usually voice signal will be enhanced when the reverberation phenomenon, the speaker in order to eliminate the background reverberation, and had to frequently deflect the direction of the head, this will cause the impulse response of changing. We combine the blind deconvolution and spectral elimination method to improve the filtering effect of the inverse filter. We use the correlation coefficient matrix between the input speech signal to calculate the stable, accurate inverse filter of the indoor impulse response, and these input signals do not need to measure the room impulse response can be observed. Inverse filtering to eliminate early reflections, these reflections contains most of the energy in the reverberation. After the spectrum elimination method to suppress the tail reverberation of the signal after the inverse filtering. Performance in the actual adaptation of the method by specific experimental validation results show that the blind deconvolution and spectrum to eliminate )

基于GMM的MFCC算法的说话人识别，Maltab的高斯混合模型，12维。(MFCC speaker recognition algorithm based on GMM)

fastICA用于盲源分离，文件中有三个语音文件进行混合后再分离。(FastICA is used for blind source separation, in which three voice files are mixed and then separated.)